Effects of chicken feet gelatin extracted at different temperatures and wheat fiber with different particle sizes on the physicochemical properties of gels
Bibliographic record
Abstract
The objectives of this study were to determine the effects of 1) the extraction temperature (65, 75, 85, and 95°C) of chicken feet gelatin (CFG) and 2) CFG extracted at different temperatures and wheat fiber (WF) with different particle sizes (80, 250, and 500 μm) on the physicochemical properties of the resultant gels. Raw chicken feet (CF) were swelled by treatment of an acidic solution [i.e., 0.1 N HCl (pH 2)]. The CFG was extracted from the swelled CF at different temperatures. Samples of 4% CFG or a mixture of 3% CFG and 3% WF were prepared using distilled water at 42 ± 1°C and then cooled to form gels. The physicochemical properties of the prepared CFG or the gel with CFG and WF were then investigated. The results indicate that the extraction yield, protein content, and L* values for the CFG samples significantly increased as the extraction temperature increased, whereas the viscosity, melting point, and a* values decreased. For the gel with CFG and WF, the gel strength, melting point, viscosity, and L* and b* values were significantly affected (P < 0.05) by the extraction temperature of CFG, but they partially were not affected (P > 0.05) by the particle size of WF. The gel with WF and extracted CFG at 65°C had the highest (P < 0.05) gel strength, melting point, viscosity, and a* values. In conclusion, CFG or the gel with CFG and WF could be utilized to prepare gelatins or gel with different physicochemical properties by controlling the extraction temperature or particle size of WF, depending on the specific application. Moreover, with its distinct physicochemical properties, the gel with CFG and WF could possibly be used as a non-meat ingredient for fat replacement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".